New 
Senior Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Jan 07, 2025 | |
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OverviewMicrosoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. The Azure AI Speech Group brings together talents in the areas of signal processing, speech modeling, statistical modeling and Deep Learning to develop and deliver robust, natural and scalable speech recognition and translation, across a rich set of scenarios and languages. As a science team in the Speech Group, we industrialized deep learning speech technologies, and contributed key innovations to the speech community. We work on all kinds of end-to-end speech technologies, targeting on the most challenging problems by inventing new algorithms. To that end, we welcome a Senior Applied Scientist who is passionate at innovating the state-of-the-art speech modeling technologies, which impact millions of users. .In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day. We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served. Microsoft's mission is to empower every person and every organization on the planet to achieve more, and we're dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.
ResponsibilitiesYour responsibilities include developing novel speech algorithms to advance state-of-the-art speech technologies for real world user scenarios, especially in integrating speech with LLM for multimodal modeling. Helps address scalability problems by adjusting to stakeholder needs. Works with large-scale computing frameworks, data analysis systems, and modeling environments to improve models. Applies the model to real products, and then verifies effects through iterations.Experiments by putting multiple models in production and evaluating their performance. Continues to monitor how algorithm performs against expected behaviors and performance or accuracy guardrails.Embody our culture and values | |